{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:27:12Z","timestamp":1743013632053,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031220630"},{"type":"electronic","value":"9783031220647"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-22064-7_4","type":"book-chapter","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:02:04Z","timestamp":1669230124000},"page":"41-52","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques"],"prefix":"10.1007","author":[{"given":"Weikang","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. M. Mazharul Hoque","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"4_CR1","unstructured":"Belkin, N.J.: Interaction with texts: Information retrieval as information seeking behavior. In: Information Retrieval. p. 55\u201366 (1993). 10.1.1.50.6725"},{"key":"4_CR2","doi-asserted-by":"publisher","unstructured":"Swanson, D.R.: Complementary structures in disjoint science literatures. In: Proceedings of the 14th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, Chicago, IL, pp. 280\u2013289 (1991). https:\/\/doi.org\/10.1145\/122860.122889","DOI":"10.1145\/122860.122889"},{"key":"4_CR3","unstructured":"Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of AMIA Annual Symposium, pp. 17\u201321 (2001). https:\/\/pubmed.ncbi.nlm.nih.gov\/11825149\/"},{"key":"4_CR4","unstructured":"Kay Deeney. MetaMap - A Tool for Recognizing UMLS Concepts in Text. U.S. National Library of Medicine (2017). https:\/\/metamap.nlm.nih.gov\/"},{"key":"4_CR5","unstructured":"Chapman, W.W., Fiszman, M., , Dowling, J.N., Chapman, B.E., Rindflesch, T.C.: Identifying respiratory findings in emergency department reports for biosurveillance using MetaMap. Studies in Health Technology and Informatics, 107(Pt 1), pp. 487\u201391 (2004). https:\/\/pubmed.ncbi.nlm.nih.gov\/15360860\/"},{"key":"4_CR6","unstructured":"Zuccon, G., Holloway, A., Koopman , B., Nguyen, A.: Identify disorders in health records using conditional random fields and metamap. In: Proceedings of the CLEF 2013 Workshop on Cross-Language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis, pp. 1\u20138 (2013). https:\/\/eprints.qut.edu.au\/62875\/"},{"key":"4_CR7","unstructured":"Pratt, W., Yetisgen-Yildiz, M.: A study of biomedical concept identification: MetaMap vs. people. In: AMIA Annual Symposium Proceedings, pp. 529\u201333 (2003). https:\/\/pubmed.ncbi.nlm.nih.gov\/14728229\/"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Jin, W., Srihari, R.K.: Knowledge discovery across documents through concept chain queries. In: Proceedings of the Sixth IEEE International Conference on Data Mining \u2013 Workshops (ICDMW\u201906), pp. 448\u2013452 (2006). https:\/\/doi.org\/10.1109\/ICDMW.2006.105","DOI":"10.1109\/ICDMW.2006.105"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Gopalakrishnan, V., Jha, K., Jin, W., Zhang, A.: A survey on literature based discovery approaches in biomedical domain. In: Journal of Biomedical Informatics, 93, 103141 (2019). doi: https:\/\/doi.org\/10.1016\/j.jbi.2019.103141","DOI":"10.1016\/j.jbi.2019.103141"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/3-540-48119-2_8","volume-title":"FM\u201999 \u2014 Formal Methods","author":"J Philipps","year":"1999","unstructured":"Philipps, J., Rumpe, B.: Refinement of pipe-and-filter architectures. In: Wing, J.M., Woodcock, J., Davies, J. (eds.) FM 1999. LNCS, vol. 1708, pp. 96\u2013115. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-48119-2_8"},{"key":"4_CR11","unstructured":"Sanscartier, M.J., Neufeld, E.: Identifying hidden variables from contextspecific independencies. In: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, pp. 472\u2013477 (2007). 10.1.1.329.7687, Florida, USA"},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"Prakash, D., Surendran, S.: Detection and analysis of hidden activities in social networks. International Journal of Computer Applications (0975\u20138887), 77(16), 34\u201338 (2013). https:\/\/doi.org\/10.5120\/13570-1404","DOI":"10.5120\/13570-1404"},{"issue":"11","key":"4_CR13","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1093\/bioinformatics\/bty899","volume":"35","author":"M Pividori","year":"2019","unstructured":"Pividori, M., Cernadas, A., de Haro, L.A., Carrari, F., Stegmayer, G., Milone, D.H.: Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization. Bioinformatics 35(11), 1931\u20131939 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/bty899","journal-title":"Bioinformatics"},{"issue":"11","key":"4_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10661-021-09499-9","volume":"193","author":"MBA Sawaf","year":"2021","unstructured":"Sawaf, M.B.A., Kawanisi, K., Jlilati, M.N., Xiao, C., Bahreinimotlagh, M.: Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models. Environ. Monit. Assess. 193(11), 1\u201314 (2021). https:\/\/doi.org\/10.1007\/s10661-021-09499-9","journal-title":"Environ. Monit. Assess."},{"issue":"3","key":"4_CR15","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1093\/llc\/fqx052","volume":"33","author":"AH Rasekh","year":"2018","unstructured":"Rasekh, A.H., Arshia, A.H., Fakhrahmad, S.M., Sadreddini, M.H.: Mining and discovery of hidden relationships between software source codes and related textual documents. Digital Scholarship in the Humanities. 33(3), 651\u2013669 (2018). https:\/\/doi.org\/10.1093\/llc\/fqx052","journal-title":"Digital Scholarship in the Humanities."},{"key":"4_CR16","unstructured":"Gopalakrishnan, V., Jha, K., Zhang, A., Jin, W.: Generating hypothesis: Using global and local features in graph to discover new knowledge from medical literature. In: Proceedings of the 8th International Conference on Bioinformatics and Computational Biology, Las Vegas, Nevada, USA. pp. 23\u201330 (2016). 978\u20131\u2013943436\u201303\u20133"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Hu, X., Zhang, X., Yoo, I., Zhang, Y.: A semantic approach for mining hidden links from complementary and non-interactive biomedical literature. In: Proceedings of the Sixth SIAM International Conference on Data Mining, Bethesda, MD, USA, pp. 200\u2013209 (2006). https:\/\/doi.org\/10.1137\/1.9781611972764.18","DOI":"10.1137\/1.9781611972764.18"},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"i290","DOI":"10.1093\/bioinformatics\/bth914","volume":"20","author":"P Srinivasan","year":"2004","unstructured":"Srinivasan, P., Libbus, B.: Mining MEDLINE for implicit links between dietary substances and diseases. In Bioinformatics. 20, i290\u2013i296 (2004). https:\/\/doi.org\/10.1093\/bioinformatics\/bth914","journal-title":"In Bioinformatics."},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Jha, K., Jin, W.: Mining novel knowledge from biomedical literature using statistical measures and domain knowledge. In: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB \u201816). Association for Computing Machinery, New York, NY, USA, pp. 317\u2013326 (2016). https:\/\/doi.org\/10.1145\/2975167.2975200","DOI":"10.1145\/2975167.2975200"},{"issue":"1","key":"4_CR20","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1353\/pbm.1986.0087","volume":"30","author":"DR Swanson","year":"1986","unstructured":"Swanson, D.R.: Fish oil, raynaud\u2019s syndrome, and undiscovered public knowledge. Perspect. Biol. Med. 30(1), 7\u201318 (1986). https:\/\/doi.org\/10.1353\/pbm.1986.0087","journal-title":"Perspect. Biol. Med."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22064-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:02:36Z","timestamp":1669230156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22064-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031220630","9783031220647"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22064-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2022.uqcloud.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"198","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}